Janus: From Workflows to Semantic Provenance and Linked Open Data

Missier, Paolo; Sahoo, Satya S.; Zhao, Jun; Goble, Carole; Sheth, Amit P.

doi:10.1007/978-3-642-17819-1_16

Cited by 47 publications

(37 citation statements)

References 14 publications

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“…As mentioned previously, provenance annotation has so far been either entirely manual, or semi-automated with particular focus on static metadata [MSZ+10]. In [SSH08] authors describe the SPADE system where they highlight dynamic metadata, and they too address data artefacts as the source of this information.…”

Section: Related Workmentioning

confidence: 99%

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LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

Alper

Belhajjame

Goble

et al. 2015

Lecture Notes in Computer Science

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Abstract. Provenance traces captured by scientific workflows can be useful for designing, debugging and maintenance. However, our experience suggests that they are of limited use for reporting results, in part because traces do not comprise domain-specific annotations needed for explaining results, and the black-box nature of some workflow activities. We show that by basic mark-up of the data processing within activities and using a set of domain specific label generation functions, standard workflow provenance can be utilised as a platform for the labelling of data artefacts. These labels can in turn aid selection of data subsets and proxy for data descriptors for shared datasets.

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Section: Related Workmentioning

confidence: 99%

“…To this date, the approach to acquiring domain-specific annotations over workflow generated data has been either entirely manual [ZW+04] or partiallyautomated [MSZ+10]. Certain fixed characteristics at workflow description level are collected and then propagated to data generated by executions.…”

Section: Introductionmentioning

confidence: 99%

LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

Alper

Belhajjame

Goble

et al. 2015

Lecture Notes in Computer Science

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“…We chose workflows for which example input parameters are available to be able to log provenance traces for their executions. To collect data provenance for these workflows, we enacted them using sample of inputs and logged the execution traces using Janus [16], the provenance system of the Taverna workbench [18].…”

Section: Preliminary Evaluationmentioning

confidence: 99%

Fostering Scientific Workflow Preservation through Discovery of Substitute Services

Belhajjame

Goble

Soiland‐Reyes

et al. 2011

2011 IEEE Seventh International Conference on eScience

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Abstract-Scientific workflows are increasingly gaining momentum as the new paradigm for modeling and enacting scientific experiments. The value of a workflow specification does not end once it is enacted. Indeed, workflow specifications encapsulate knowledge that documents scientific experiments, and are, therefore, worth preserving.Our experience suggests that workflow preservation is frequently hampered by the volatility of the constituent service operations when these operations are supplied by third-party providers. To deal with this issue, we propose a heuristic for locating substitutes that are able to replace unavailable service operations within workflows. The proposed method uses the data links connecting inputs and outputs of service operations in existing workflow specifications to locate operations with parameters compatible with those of the missing operations. Furthermore, it exploits provenance traces collected from past executions of workflows to ensure that candidate substitutes perform tasks similar to those of the missing operations. The effectiveness of the proposed method has been empirically assessed.

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“…The Simple Network Access Rights Management (SNARM 11 ) ontology was used to capture the sharing model of myExperiment. For representing the social content of myExperiment several ontologies were reused: Dublin Core 12 , Friend of a Friend 13 , Semantically Inter-linked Online Communities (SIOC 14 ), and the Open Archives Initi Reuse and Exchange ontologies/schemata (OAI/ORE 15 ). These shared ontologies facilitate co-reference resolution, which is one of the major tasks on the Semantic Web.…”

Section: Myexperiments and Biocatalogue: Repositories For Digital Protmentioning

confidence: 99%

A Linked Data Approach to Sharing Workflows and Workflow Results

Roos

Bechhofer

Zhao

et al. 2010

Lecture Notes in Computer Science

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Abstract.A bioinformatics analysis pipeline is often highly elaborate, due to the inherent complexity of biological systems and the variety and size of laboratory publications would be highly beneficial to bioinformatics, for evaluating evidence and examining data across related experiments, while introducing the potential to find associated resources and integrate them as data and services. We present initial steps towards preserving bioinformatics design of a data analysis pipeline, and RDF to link the workflow, its component services, run-time provenance, and a personalized biological interpretation of the results. An example shows the reproduction of the unique graph of an analysis procedure, its results, provenance, and personal interpretation of a text mining experiment. It links data from Taverna, myExperiment.org, BioCatalogue.org, and ConceptWiki.org. The approach is relat -unobtrusive to bioinformatics users.

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Janus: From Workflows to Semantic Provenance and Linked Open Data

Cited by 47 publications

References 14 publications

LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

Fostering Scientific Workflow Preservation through Discovery of Substitute Services

A Linked Data Approach to Sharing Workflows and Workflow Results

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